41 research outputs found
Fundamental Imaging Limits of Radio Telescope Arrays
The fidelity of radio astronomical images is generally assessed by practical
experience, i.e. using rules of thumb, although some aspects and cases have
been treated rigorously. In this paper we present a mathematical framework
capable of describing the fundamental limits of radio astronomical imaging
problems. Although the data model assumes a single snapshot observation, i.e.
variations in time and frequency are not considered, this framework is
sufficiently general to allow extension to synthesis observations. Using tools
from statistical signal processing and linear algebra, we discuss the
tractability of the imaging and deconvolution problem, the redistribution of
noise in the map by the imaging and deconvolution process, the covariance of
the image values due to propagation of calibration errors and thermal noise and
the upper limit on the number of sources tractable by self calibration. The
combination of covariance of the image values and the number of tractable
sources determines the effective noise floor achievable in the imaging process.
The effective noise provides a better figure of merit than dynamic range since
it includes the spatial variations of the noise. Our results provide handles
for improving the imaging performance by design of the array.Comment: 12 pages, 8 figure
Multisource Self-calibration for Sensor Arrays
Calibration of a sensor array is more involved if the antennas have direction
dependent gains and multiple calibrator sources are simultaneously present. We
study this case for a sensor array with arbitrary geometry but identical
elements, i.e. elements with the same direction dependent gain pattern. A
weighted alternating least squares (WALS) algorithm is derived that iteratively
solves for the direction independent complex gains of the array elements, their
noise powers and their gains in the direction of the calibrator sources. An
extension of the problem is the case where the apparent calibrator source
locations are unknown, e.g., due to refractive propagation paths. For this
case, the WALS method is supplemented with weighted subspace fitting (WSF)
direction finding techniques. Using Monte Carlo simulations we demonstrate that
both methods are asymptotically statistically efficient and converge within two
iterations even in cases of low SNR.Comment: 11 pages, 8 figure
Fast gain calibration in radio astronomy using alternating direction implicit methods: Analysis and applications
Context. Modern radio astronomical arrays have (or will have) more than one
order of magnitude more receivers than classical synthesis arrays, such as the
VLA and the WSRT. This makes gain calibration a computationally demanding task.
Several alternating direction implicit (ADI) approaches have therefore been
proposed that reduce numerical complexity for this task from
to , where is the number of receive paths to be
calibrated.
Aims. We present an ADI method, show that it converges to the optimal
solution, and assess its numerical, computational and statistical performance.
We also discuss its suitability for application in self-calibration and report
on its successful application in LOFAR standard pipelines.
Methods. Convergence is proved by rigorous mathematical analysis using a
contraction mapping. Its numerical, algorithmic, and statistical performance,
as well as its suitability for application in self-calibration, are assessed
using simulations.
Results. Our simulations confirm the complexity and
excellent numerical and computational properties of the algorithm. They also
confirm that the algorithm performs at or close to the Cramer-Rao bound (CRB,
lower bound on the variance of estimated parameters). We find that the
algorithm is suitable for application in self-calibration and discuss how it
can be included. We demonstrate an order-of-magnitude speed improvement in
calibration over traditional methods on actual LOFAR data.
Conclusions. In this paper, we demonstrate that ADI methods are a valid and
computationally more efficient alternative to traditional gain calibration
method and we report on its successful application in a number of actual data
reduction pipelines.Comment: accepted for publication in Astronomy & Astrophysic
Redundancy Calibration of Phased Array Stations
Our aim is to assess the benefits and limitations of using the redundant
visibility information in regular phased array systems for improving the
calibration.
Regular arrays offer the possibility to use redundant visibility information
to constrain the calibration of the array independent of a sky model and a beam
models of the station elements. It requires a regular arrangement in the
configuration of array elements and identical beam patterns.
We revised a calibration method for phased array stations using the redundant
visibility information in the system and applied it successfully to a LOFAR
station. The performance and limitations of the method were demonstrated by
comparing its use on real and simulated data. The main limitation is the mutual
coupling between the station elements, which leads to non-identical beams and
stronger baseline dependent noise. Comparing the variance of the estimated
complex gains with the Cramer-Rao Bound (CRB) indicates that redundancy is a
stable and optimum method for calibrating the complex gains of the system.
Our study shows that the use of the redundant visibility does improve the
quality of the calibration in phased array systems. In addition it provides a
powerful tool for system diagnostics. Our results demonstrate that designing
redundancy in both the station layout and the array configuration of future
aperture arrays is strongly recommended. In particular in the case of the
Square Kilometre Array with its dynamic range requirement which surpasses any
existing array by an order of magnitude.Comment: 16 pages, 15 figures, accepted for publication in the A&A in Section
13, acceptance date: 1st May 2012. NOTE: Please contact the first author for
high resolution figure
Calibration Challenges for Future Radio Telescopes
Instruments for radio astronomical observations have come a long way. While
the first telescopes were based on very large dishes and 2-antenna
interferometers, current instruments consist of dozens of steerable dishes,
whereas future instruments will be even larger distributed sensor arrays with a
hierarchy of phased array elements. For such arrays to provide meaningful
output (images), accurate calibration is of critical importance. Calibration
must solve for the unknown antenna gains and phases, as well as the unknown
atmospheric and ionospheric disturbances. Future telescopes will have a large
number of elements and a large field of view. In this case the parameters are
strongly direction dependent, resulting in a large number of unknown parameters
even if appropriately constrained physical or phenomenological descriptions are
used. This makes calibration a daunting parameter estimation task, that is
reviewed from a signal processing perspective in this article.Comment: 12 pages, 7 figures, 20 subfigures The title quoted in the meta-data
is the title after release / final editing
Classification of Radio Galaxies with trainable COSFIRE filters
Radio galaxies exhibit a rich diversity of characteristics and emit radio
emissions through a variety of radiation mechanisms, making their
classification into distinct types based on morphology a complex challenge. To
address this challenge effectively, we introduce an innovative approach for
radio galaxy classification using COSFIRE filters. These filters possess the
ability to adapt to both the shape and orientation of prototype patterns within
images. The COSFIRE approach is explainable, learning-free, rotation-tolerant,
efficient, and does not require a huge training set. To assess the efficacy of
our method, we conducted experiments on a benchmark radio galaxy data set
comprising of 1180 training samples and 404 test samples. Notably, our approach
achieved an average accuracy rate of 93.36\%. This achievement outperforms
contemporary deep learning models, and it is the best result ever achieved on
this data set. Additionally, COSFIRE filters offer better computational
performance, 20 fewer operations than the DenseNet-based
competing method (when comparing at the same accuracy). Our findings underscore
the effectiveness of the COSFIRE filter-based approach in addressing the
complexities associated with radio galaxy classification. This research
contributes to advancing the field by offering a robust solution that
transcends the orientation challenges intrinsic to radio galaxy observations.
Our method is versatile in that it is applicable to various image
classification approaches.Comment: 11 pages, 7 figures, submitted for review at MNRAS journa
Deep supervised hashing for fast retrieval of radio image cubes
The shear number of sources that will be detected by next-generation radio
surveys will be astronomical, which will result in serendipitous discoveries.
Data-dependent deep hashing algorithms have been shown to be efficient at image
retrieval tasks in the fields of computer vision and multimedia. However, there
are limited applications of these methodologies in the field of astronomy. In
this work, we utilize deep hashing to rapidly search for similar images in a
large database. The experiment uses a balanced dataset of 2708 samples
consisting of four classes: Compact, FRI, FRII, and Bent. The performance of
the method was evaluated using the mean average precision (mAP) metric where a
precision of 88.5\% was achieved. The experimental results demonstrate the
capability to search and retrieve similar radio images efficiently and at
scale. The retrieval is based on the Hamming distance between the binary hash
of the query image and those of the reference images in the database.Comment: 4 pages, 4 figure
Advances on the morphological classification of radio galaxiesreview: A review
Modern radio telescopes will generate, on a daily basis, data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of machine learning to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of machine/deep learning in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study provides a comprehensive and concise overview of the use of machine learning techniques for the morphological classification of radio galaxies. It summarizes the recent literature on this topic, highlighting the main challenges, achievements, state-of-the-art methods, and the future research directions in the field. The application of machine learning in radio astronomy has led to a new paradigm shift and a revolution in the automation of complex data processes. However, the optimal exploitation of machine/deep learning in radio astronomy, calls for continued collaborative efforts in the creation of high-resolution annotated data sets. This is especially true in the case of modern telescopes like MeerKAT and the LOw-Frequency ARray (LOFAR). Additionally, it is important to consider the potential benefits of utilizing multi-channel data cubes and algorithms that can leverage massive datasets without relying solely on annotated datasets for radio galaxy classification.<br/
Measurement of the LOFAR-HBA beam patterns using an unmanned aerial vehicle in the near field
An unmanned aerial vehicle (UAV) is exploited to characterize in situ the high-band
antennas (HBAs) of the low-frequency array (LOFAR) CS302 station located in Exloo, The
Netherlands. The size of an HBA array is about 30 m. The Fraunhofer distance (a few kilometers)
is not reachable in the frequency band (120 to 240 MHz) within the flight regulation limits.
Therefore, far-field patterns cannot be directly measured. The UAV, equipped with an radio frequency
synthesizer and a dipole antenna, flies in the near-field region of the considered array.
Measurement of three different frequencies (124, 150, and 180 MHz) is efficiently made during
the same UAV flight. The near-field focusing method is exploited to validate the far-field pattern
of the array under test within an angular range around the beam axis. Such a technique avoids
both the time consuming λ∕2 sampling of the aperture field and the further application of computationally
heavy near-field to far-field transformations. The array beam is well reconstructed in
the main lobe and first sidelobes within a 2D scan plane sampled with a radial raster. A further
postprocessing technique is proposed and validated on a subarray of HBAs. It suggests efficient
ways for the future characterization of regular aperture arrays for SKA-MID Phase 2